clear all;
close all;

% %test for P parameters initialization
% for i=1:10000
%     P = featureParametersInitialization(50,4);
%     R = sum((P(:,1) + P(:,2)) > 50);
%     if(R ~= 0)
%         P
%         break;
%     end;
% end;

% % test for feature program
% % TODO 
% for i=1:10000
%     try
% dataPath = 'rawdata.csv';
% DATA = csvread(dataPath);
% P = [2 3; 4 2; 5 5; 1 1];
% [m, n] = size(DATA);
% %P = featureParametersInitialization(m, n);
% [FY, FX] = featureProgram(DATA,P);
%     catch
%         disp('error');
%         pause;
%     end;
% end;

% % test for feature program teste 2
% % TODO
% dataPath = 'rawdata.csv';
% DATA = csvread(dataPath);
% P = [2 3; 4 2; 5 5; 1 1];
% [FY, F] = featureProgram(DATA(:,1),DATA,P);


% %test for prediction model regression
% dataPath = 'testLR.csv';
% DATA = csvread(dataPath);
% [W, predCost] = linearRegressionModel(DATA(:,2),DATA(:,1), 100, 0.003, 0);
% figure,plot(predCost);
% figure, scatter(DATA(:,2),DATA(:,1)), hold on;
% H = [ones(size(DATA,1),1) DATA(:,2)] * W;
% plot(DATA(:,2),H);
% 

%test for feature optimization
%TODO
%
%



%test for model prediction
%TODO
%
%
%


% %test for sythetic data Generator
% [X, Y, P, W] = syntheticDataGenerator(100,4);



% %test of approach 2
% %Y Target Variable
% Y = csvread('Data\DataApp2\3cities\Y.csv');
% 
% %X rawdata training data set
% X = csvread('Data\DataApp2\3cities\X.csv');
% 
% [Woptim, WCostO] = approach2(Y, X, 7, 400, 0.0001, 1);
% figure, plot(WCostO);
% 
% [H cost] = modelPredictionAPP2(Y, X, 7, Woptim);
% disp(cost);

